1. Technical Field
Example aspects herein relate in general to medical image processing, and, more particularly, relate to systems, methods, apparatuses, and computer program products for computer aided lung nodule detection in tomosynthesis images.
2. Technical Background
Tomosynthesis is an emerging imaging modality for chest examinations. Using this modality, more lung lesions can be detected than when using computerized radiography (CR) or digital radiography (DR). Moreover, tomosynthesis scans result in far lower x-ray dosages than do computed tomography (CT) scans.
Typically, more than 40 images (or “slices”) are reconstructed from a tomosynthesis scan. Because of this large amount of data, and the potential that a radiologist may miss lesions while reviewing images, computer aided detection (CAD) systems for lung nodule detection in tomosynthesis images may be used to fully exploit the advantages provided by tomosynthesis.
However, existing lung nodule detection methods for CR/DR and CT modalities are not easily applied to tomosynthesis because tomosynthesis images have different characteristics than CR, DR, and CT images. For example, a complete tomosynthesis scan may have a slice thickness of 10 mm or greater and a slice interval of around 5 mm. These numbers yield approximately 40 slices in a tomosynthesis scan, which is far less than the number of slices in a typical CT scan. Furthermore, due to a blurring effect in tomosynthesis images, an anatomic structure may appear blurred in images other than the corresponding focal plane of the structure. That is, a structure may appear mostly clear in its focal image, but blurred in neighboring images. Accordingly, there is a need for lung nodule detection that accounts for these characteristics of tomosynthesis images.
Moreover, lung nodule detection generally is performed on medical images in which the lungs have been segmented from other anatomic structures like the heart and spine. Lung segmentation approaches for other modalities, such as chest x-ray images or chest CT images, cannot readily be applied to tomosynthesis images. In a tomosynthesis chest image, lung areas typically do not show strong contrast as in chest x-ray images, and there is 3-dimensional (3D) information available in tomosynthesis images, while in chest x-ray images there is no such 3D information. Furthermore, it does not have a calibrated pixel value in Hounsfield units, as CT chest images do. Accordingly there is a need for lung segmentation that identifies both the lung area and the rib structures.